2018/07/31 by Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +2 · 25 citations
Computer Science · Economics, Econometrics and Finance · Engineering · #Artificial Intelligence in Games #Artificial intelligence #Computer science #Game design #Game mechanics #Human Motion and Animation #Human–computer interaction #Jump #Sports Analytics and Performance #cs.AI
paper · pdf · doi:10.1145/3235765.3235820
8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018)
openalex publication_date 2018/08/07 · arxiv created 2018/10/01 · arxiv updated 2018/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.